Bayesian Local Sampling-Based Planning

Sampling-Based Planning and Local Reactive Strategies for Environment Manipulation

We address the motion planning problem for a single robot that manipulates passive objects in an otherwise static environment. We handle uncertainties in passive object responses while abstracting their dynamics by creating a roadmap that encodes the possible passive object responses. The robot extracts reference paths from the passive roadmap to plan approaching motions with an active roadmap....

Sampling-Based Motion Planning

Sampling-Based Planning and Control

In this paper, we describe a planning and control approach in terms of sampling using Rapidlyexploring Random Trees (RRTs), which were introduced in [7, 9]. We review RRTs for motion planning and show how to use them to solve standard nonlinear control problems. We extend them to the case of hybrid systems and describe our modifications to LaValle’s Motion Strategy Library to allow for hybrid m...

Sampling-Based Finger Gaits Planning

F gait is a strategy for a multi-fingered robotic hand to change its grasp status sequentially by relocating its fingers, which gives the hand more dexterity to perform large scale or complicated dextrous manipulations (Hong et al. 1990). In a finger gait, the robotic hand must achieve the desired configuration of both the object and the fingers, which involves the manipulation of the object as...

Local Importance Sampling in Multiply Sectioned Bayesian Networks

The multiply sectioned Bayesian network (MSBN) is a well-studied model for probability reasoning in a multiagent setting. Exact inference, however, becomes difficult as the problem domain grows larger and more complex. We address this issue by integrating approximation techniques with the MSBN Linked Junction Tree Forest (LJF) framework. In particular, we investigate the application of importan...